Air Quality Prediction .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=pd.read_csv('Train.csv')\n",
    "dft=pd.read_csv('Test.csv')\n",
    "vt=dft.values\n",
    "v=df.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of feature of data set is 5\n"
     ]
    }
   ],
   "source": [
    "x=v[:,:-1]\n",
    "y=v[:,-1:]\n",
    "x_test=vt[:,:-1]\n",
    "y_test=vt[:,-1:]\n",
    "print(\"Number of feature of data set is\",x.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1600, 6)"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X=(x-x.mean())/x.std()\n",
    "X_test=(x_test-x_test.mean())/x_test.std()\n",
    "Y =y\n",
    "Y_test=y_test\n",
    "X=np.insert(X,0,1,axis=1)\n",
    "X_test=np.insert(X_test,0,1,axis=1)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Gradient Desent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hypothesis(X, theta):\n",
    "    return  np.dot(X,theta.T)\n",
    "def cost(X,Y,theta):\n",
    "    m=X.shape[0]\n",
    "    hp=hypothesis(X,theta)\n",
    "    c=(hp-Y)**2\n",
    "    return np.sum(c)/(2*m)\n",
    "def gradient_desent(X,Y , learning_rate=0.01):\n",
    "    theta=np.zeros((1,X.shape[1]))\n",
    "    m=X.shape[0]\n",
    "    cost_list= np.zeros(1000)\n",
    "    for i in range(1000):\n",
    "        hp=hypothesis(X,theta)\n",
    "        theta = theta - (learning_rate/m) * (np.dot(X.T,(hp - Y))).T\n",
    "        cost_list[i] = cost(X, Y, theta)\n",
    "    return theta, cost_list\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Batch gradient desent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def stochasticgrad(x,theta,y):\n",
    "    grad_thetas=np.zeros(theta.shape)\n",
    "    grad_bias=0.0\n",
    "    hx=hypothesis(x,theta)\n",
    "    grad_thetas+=(y-hx)*x\n",
    "    return grad_thetas\n",
    "def batchgradient(x,y,theta,batchsize=1000,learning_rate=0.001):\n",
    "    grad_w=np.zeros(theta.shape)\n",
    "    a=grad_w\n",
    "    \n",
    "    m=x.shape[0]\n",
    "    \n",
    "    error_list=[]\n",
    "    for j in range(400):\n",
    "        \n",
    "        indices=np.arange(m)\n",
    "        np.random.shuffle(indices)\n",
    "        array=indices[:batchsize]\n",
    "\n",
    "        cos=0\n",
    "        for i in array:\n",
    "            \n",
    "            h=hypothesis(x[i],theta)\n",
    "            grad_w=stochasticgrad(x[i],theta,y[i])\n",
    "            a+=grad_w\n",
    "\n",
    "            cos+=cost(x[i],y[i],theta)\n",
    "        a/=batchsize\n",
    "        \n",
    "        theta=theta+learning_rate*a\n",
    "     \n",
    "        error_list.append(cos/batchsize)\n",
    "    return error_list,theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_theta , cost_list= gradient_desent(X,Y)\n",
    "final_cost=cost(X,Y,final_theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "final hypothesis is 509.200570463\n"
     ]
    }
   ],
   "source": [
    "hp=hypothesis(X,final_theta)\n",
    "print(\"final hypothesis is\",np.sum(hp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "207.776673737\n"
     ]
    }
   ],
   "source": [
    "print(final_cost)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7m4TBzsMKABGJPgVAmll5OSytKuZNBYCIxIAC4AJXz9edQCISDwqAC3xwwRyOdJ6m5cTp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2361ab78128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(cost_list)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.49074643,  29.8408354 ,  93.16196034,   8.32269365,\n",
       "         45.00820867,   2.53129205]])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.25792236,  29.41734835,  91.38500463,   7.96037096,\n",
       "         44.07350709,   2.21885545]])"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t=np.zeros((1,X.shape[1]))\n",
    "bcost_list,bfinal_theta=batchgradient(X,Y,t,batchsize=64,learning_rate=0.01)\n",
    "bfinal_theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JPglfIBAADonIYGPMQauKpzTk/GEhr88HDrR9U2PMXGAuwJQpU7qlnuWmcwq46ZyCsLTQ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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x23619865400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bcost_list)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "coefficients of theta- [ 29.8408354   93.16196034   8.32269365  45.00820867   2.53129205]\n",
      "intercept of theta- 1.49074642538\n",
      "hypothesis function [[ -57.87084727]\n",
      " [ -89.45473064]\n",
      " [  53.95018198]\n",
      " ..., \n",
      " [-115.65900246]\n",
      " [ -64.27841029]\n",
      " [-116.10771024]]\n"
     ]
    }
   ],
   "source": [
    "print(\"coefficients of theta-\",final_theta[0,1:])\n",
    "print(\"intercept of theta-\",final_theta[0,0])\n",
    "print(\"hypothesis function\",hypothesis(X,final_theta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def Coefficient_of_Determination(Y,X,final_theta):\n",
    "    Y_predict=hypothesis(X, final_theta)\n",
    "    m=X.shape[0]\n",
    "    re=np.sum((Y-Y_predict)**2)\n",
    "    sre=np.sum((Y-Y.mean())**2)\n",
    "    r= 1- (re/sre)\n",
    "    return r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Score of gradient desent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score using Traning data 0.966093964393\n",
      "score using Test data 0.968528055101\n"
     ]
    }
   ],
   "source": [
    "r= Coefficient_of_Determination(Y,X,final_theta)\n",
    "print(\"score using Traning data\",r)\n",
    "rt=Coefficient_of_Determination(Y_test,X_test,final_theta)\n",
    "print(\"score using Test data\",rt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score of test data is better\n",
      "[[ -8.61204418e+01]\n",
      " [ -1.63939206e+01]\n",
      " [ -6.73801809e+01]\n",
      " [ -2.41157407e+02]\n",
      " [ -2.85455633e+01]\n",
      " [ -1.77447216e+02]\n",
      " [ -1.58110724e+02]\n",
      " [  1.89121515e+01]\n",
      " [ -1.03163108e+01]\n",
      " [ -6.25798862e+01]\n",
      " [  1.06234502e+02]\n",
      " [  2.04391380e+01]\n",
      " [  1.60808023e+02]\n",
      " [ -9.51495587e+01]\n",
      " [  4.70567821e+01]\n",
      " [  3.26618048e+01]\n",
      " [  1.31868001e+02]\n",
      " [  1.09515652e+02]\n",
      " [  4.48100255e+01]\n",
      " [  2.84382460e+01]\n",
      " [  6.57859600e+01]\n",
      " [  3.47991490e+01]\n",
      " [  6.44101023e+01]\n",
      " [ -8.39576588e+01]\n",
      " [  1.16581742e+02]\n",
      " [  2.24322221e+01]\n",
      " [ -3.42734742e+01]\n",
      " [  1.32627402e+02]\n",
      " [ -4.63831110e+01]\n",
      " [  1.35682036e+02]\n",
      " [  4.36488179e+01]\n",
      " [ -2.57562679e+01]\n",
      " [ -1.65610316e+02]\n",
      " [ -1.22393713e+02]\n",
      " [ -9.07488768e+01]\n",
      " [ -5.05792262e+01]\n",
      " [ -5.41094621e+01]\n",
      " [  7.23442844e+01]\n",
      " [ -1.18889050e+02]\n",
      " [ -1.68184211e+00]\n",
      " [  1.69436558e+02]\n",
      " [ -8.46431249e+01]\n",
      " [ -1.24871711e+02]\n",
      " [  3.75477114e+01]\n",
      " [ -6.39047026e+01]\n",
      " [  7.88172392e+01]\n",
      " [  1.04529570e+02]\n",
      " [  1.30215197e+02]\n",
      " [ -1.28141060e+01]\n",
      " [ -9.98226630e+01]\n",
      " [ -3.67623488e+01]\n",
      " [  1.86000080e+02]\n",
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      " [ -7.09030362e+01]\n",
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      " [ -1.75179490e+02]\n",
      " [  2.61427728e+01]\n",
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      " [ -6.39066513e+01]\n",
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      " [ -5.12958473e+01]\n",
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      " [  5.09042002e+01]\n",
      " [  5.53418977e+01]\n",
      " [  3.11402070e+01]\n",
      " [  1.29434932e+02]\n",
      " [  2.74096131e+01]\n",
      " [  8.01473205e+01]\n",
      " [ -7.57027425e+01]\n",
      " [ -8.31802221e+01]\n",
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      " [ -2.08255840e+02]\n",
      " [ -5.67209693e+00]\n",
      " [  1.69641657e+02]\n",
      " [ -2.00943534e+01]\n",
      " [ -1.74560824e+02]\n",
      " [  3.02068542e+01]\n",
      " [  1.40036795e+02]\n",
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      " [  6.37978689e+01]\n",
      " [  6.49869177e-02]\n",
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      " [ -4.22876619e+01]\n",
      " [  9.02259899e+01]\n",
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      " [ -9.56847145e+01]\n",
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      " [  9.23487638e+01]\n",
      " [  1.54352975e+02]\n",
      " [  9.10021271e+00]\n",
      " [  7.55674220e+01]\n",
      " [ -2.39214229e+01]\n",
      " [  6.40064375e+01]\n",
      " [  1.11788930e+01]\n",
      " [  8.66572735e+01]\n",
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      " [  2.15762278e+02]\n",
      " [ -4.98338347e+01]\n",
      " [ -7.10689756e+01]\n",
      " [  2.37463336e+02]\n",
      " [  1.84379819e+02]\n",
      " [  2.75650354e+01]\n",
      " [  1.32648235e+02]\n",
      " [ -1.73225284e+02]\n",
      " [ -1.40760115e+02]\n",
      " [ -1.32188215e+02]\n",
      " [ -9.18755722e+00]\n",
      " [ -3.04552750e+01]\n",
      " [  1.61356826e+02]\n",
      " [ -5.26961609e+01]\n",
      " [  1.60217709e+01]\n",
      " [ -2.49884750e+01]\n",
      " [  9.85600071e+01]\n",
      " [  8.30444800e+00]\n",
      " [  1.49157289e+02]\n",
      " [ -5.85244234e+01]\n",
      " [  2.97259170e+01]\n",
      " [ -7.65583180e+01]\n",
      " [ -1.68077993e+02]\n",
      " [  1.63585097e+02]\n",
      " [  9.92535647e+00]\n",
      " [ -3.51162916e+00]\n",
      " [ -8.84912924e+01]\n",
      " [ -5.31460904e+01]\n",
      " [ -6.83605283e+01]\n",
      " [  6.40835865e+01]\n",
      " [  1.28759709e+02]\n",
      " [ -2.45986858e+01]\n",
      " [ -1.44544197e+02]\n",
      " [  1.57271632e+01]\n",
      " [  9.21448005e+01]\n",
      " [  7.40921596e+01]\n",
      " [  1.72947207e+02]\n",
      " [  1.63727440e+01]\n",
      " [ -8.58704326e+01]\n",
      " [  1.53563157e+02]\n",
      " [ -1.10238032e+02]\n",
      " [ -1.71982642e+02]\n",
      " [  1.07177261e+02]\n",
      " [ -7.75204516e+01]\n",
      " [ -3.25655118e+01]\n",
      " [  1.15967590e+02]\n",
      " [ -2.08941687e+02]\n",
      " [ -5.89940228e+01]\n",
      " [  6.05019517e+01]\n",
      " [ -1.66080872e+02]\n",
      " [ -1.09365532e+02]\n",
      " [ -1.55258199e+02]\n",
      " [  4.70980697e+01]\n",
      " [  7.87370129e+01]\n",
      " [  3.43126078e+01]\n",
      " [  1.01769091e+01]\n",
      " [ -2.11306936e+01]\n",
      " [ -4.49765941e+01]\n",
      " [ -3.10889461e+02]\n",
      " [  3.83259499e+01]\n",
      " [  2.66408468e+02]\n",
      " [  1.23884518e+02]\n",
      " [ -3.83525007e+01]\n",
      " [  6.92247229e+01]\n",
      " [  2.10914577e+02]\n",
      " [ -1.66648911e+01]\n",
      " [ -1.59612781e+02]\n",
      " [  1.33368104e+02]\n",
      " [ -2.55963183e+02]\n",
      " [ -1.62675973e+02]\n",
      " [ -8.62649664e+01]\n",
      " [ -2.95728697e+01]\n",
      " [ -4.57699274e+01]\n",
      " [ -6.07789897e+01]\n",
      " [ -5.23958409e+01]\n",
      " [  6.39642875e+01]\n",
      " [  4.52749112e+01]\n",
      " [  4.57054994e+01]\n",
      " [ -1.23686059e+02]\n",
      " [  4.19295293e+01]\n",
      " [  1.69699045e+01]\n",
      " [  8.81742358e+00]\n",
      " [  2.39994365e+02]\n",
      " [  1.56899909e+02]\n",
      " [ -7.47530650e+01]\n",
      " [  6.09558901e+01]\n",
      " [ -2.87813021e+01]\n",
      " [ -1.19090154e+02]\n",
      " [ -3.95932284e+01]\n",
      " [ -2.30973215e+02]\n",
      " [ -1.08190494e+02]\n",
      " [ -2.92077224e+01]\n",
      " [  2.36202439e+01]\n",
      " [ -2.07332660e+00]\n",
      " [ -3.38834941e+01]\n",
      " [ -8.44850627e+01]\n",
      " [ -6.12897593e+00]\n",
      " [ -1.52159794e+00]\n",
      " [ -2.30039562e+01]\n",
      " [  5.54642355e+01]\n",
      " [  6.35818106e+01]\n",
      " [ -2.18357911e+02]\n",
      " [ -8.53413504e+01]\n",
      " [ -1.14368591e+01]\n",
      " [ -1.28889988e+02]\n",
      " [ -4.66711503e+01]\n",
      " [ -6.43262024e+01]\n",
      " [  1.11852242e+02]\n",
      " [ -1.23364383e+02]]\n"
     ]
    }
   ],
   "source": [
    "print(\"score of test data is better\")\n",
    "Y_predict=hypothesis(X_test, final_theta)\n",
    "print(Y_predict) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "score using batch gradient desent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score using Traning data 0.965726043675\n",
      "score using Test data 0.96818356129\n"
     ]
    }
   ],
   "source": [
    "br= Coefficient_of_Determination(Y,X,bfinal_theta)\n",
    "print(\"score using Traning data\",br)\n",
    "brt=Coefficient_of_Determination(Y_test,X_test,bfinal_theta)\n",
    "print(\"score using Test data\",brt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score of test data is better\n",
      "[[ -8.47526553e+01]\n",
      " [ -1.64142022e+01]\n",
      " [ -6.61313047e+01]\n",
      " [ -2.36814963e+02]\n",
      " [ -2.82468818e+01]\n",
      " [ -1.74564689e+02]\n",
      " [ -1.54877582e+02]\n",
      " [  1.90668013e+01]\n",
      " [ -1.03600316e+01]\n",
      " [ -6.20450483e+01]\n",
      " [  1.04526139e+02]\n",
      " [  1.99947992e+01]\n",
      " [  1.57777060e+02]\n",
      " [ -9.36309000e+01]\n",
      " [  4.61907840e+01]\n",
      " [  3.18879886e+01]\n",
      " [  1.29166487e+02]\n",
      " [  1.06677445e+02]\n",
      " [  4.35619408e+01]\n",
      " [  2.77140224e+01]\n",
      " [  6.40163789e+01]\n",
      " [  3.37055586e+01]\n",
      " [  6.31175195e+01]\n",
      " [ -8.25681644e+01]\n",
      " [  1.14008131e+02]\n",
      " [  2.13852649e+01]\n",
      " [ -3.40094242e+01]\n",
      " [  1.30071614e+02]\n",
      " [ -4.61673898e+01]\n",
      " [  1.32251666e+02]\n",
      " [  4.26020267e+01]\n",
      " [ -2.61165810e+01]\n",
      " [ -1.62998437e+02]\n",
      " [ -1.19619231e+02]\n",
      " [ -8.95407733e+01]\n",
      " [ -4.91576251e+01]\n",
      " [ -5.36855211e+01]\n",
      " [  7.00879214e+01]\n",
      " [ -1.17059450e+02]\n",
      " [ -1.99516088e+00]\n",
      " [  1.66158360e+02]\n",
      " [ -8.31972357e+01]\n",
      " [ -1.22573641e+02]\n",
      " [  3.68376073e+01]\n",
      " [ -6.25633653e+01]\n",
      " [  7.64302941e+01]\n",
      " [  1.02466413e+02]\n",
      " [  1.27041794e+02]\n",
      " [ -1.30181163e+01]\n",
      " [ -9.82045326e+01]\n",
      " [ -3.61306666e+01]\n",
      " [  1.81943895e+02]\n",
      " [ -1.62369938e+01]\n",
      " [  1.42341273e+02]\n",
      " [ -1.53032834e+02]\n",
      " [ -1.70766226e+02]\n",
      " [  3.85897156e+01]\n",
      " [ -1.55203242e+02]\n",
      " [ -2.12032250e+01]\n",
      " [  3.76758978e+01]\n",
      " [ -8.91302850e+01]\n",
      " [ -2.95618789e+01]\n",
      " [ -5.93935103e+01]\n",
      " [  2.81007316e+01]\n",
      " [  1.16802004e+02]\n",
      " [ -8.45979720e+01]\n",
      " [  1.56847976e+02]\n",
      " [ -4.60556960e+01]\n",
      " [ -1.70354829e+02]\n",
      " [  2.13856307e+02]\n",
      " [ -3.45692346e+01]\n",
      " [  1.07882227e+02]\n",
      " [  7.68507142e+01]\n",
      " [ -1.06920528e+02]\n",
      " [  1.21044243e+02]\n",
      " [ -6.38898931e+01]\n",
      " [ -1.09348567e+02]\n",
      " [  5.47859670e+01]\n",
      " [  2.01259865e+02]\n",
      " [ -7.57646815e+01]\n",
      " [ -3.53014185e+01]\n",
      " [ -4.03642602e+01]\n",
      " [  1.92633255e+01]\n",
      " [  4.99151522e+01]\n",
      " [  1.24304544e+02]\n",
      " [ -3.08321608e+00]\n",
      " [  1.11643180e+02]\n",
      " [ -2.93490501e+01]\n",
      " [ -3.95569301e+01]\n",
      " [ -8.82957678e+01]\n",
      " [  1.09158991e+02]\n",
      " [  8.86861584e+01]\n",
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      " [  1.30104045e+02]\n",
      " [ -2.51340494e+02]\n",
      " [ -1.59964703e+02]\n",
      " [ -8.44516016e+01]\n",
      " [ -2.91634364e+01]\n",
      " [ -4.56037338e+01]\n",
      " [ -5.95484125e+01]\n",
      " [ -5.11391108e+01]\n",
      " [  6.21733754e+01]\n",
      " [  4.42569311e+01]\n",
      " [  4.46289025e+01]\n",
      " [ -1.21465915e+02]\n",
      " [  4.10806440e+01]\n",
      " [  1.67336951e+01]\n",
      " [  7.94541927e+00]\n",
      " [  2.35055327e+02]\n",
      " [  1.53252795e+02]\n",
      " [ -7.35730445e+01]\n",
      " [  5.95225633e+01]\n",
      " [ -2.90062485e+01]\n",
      " [ -1.16911239e+02]\n",
      " [ -3.92122106e+01]\n",
      " [ -2.26930329e+02]\n",
      " [ -1.06713141e+02]\n",
      " [ -2.85921749e+01]\n",
      " [  2.32136017e+01]\n",
      " [ -2.34504043e+00]\n",
      " [ -3.35753895e+01]\n",
      " [ -8.31342708e+01]\n",
      " [ -6.32464136e+00]\n",
      " [ -2.04349107e+00]\n",
      " [ -2.27370467e+01]\n",
      " [  5.39372550e+01]\n",
      " [  6.22410480e+01]\n",
      " [ -2.14085708e+02]\n",
      " [ -8.36226489e+01]\n",
      " [ -1.21458358e+01]\n",
      " [ -1.27130035e+02]\n",
      " [ -4.59729570e+01]\n",
      " [ -6.39628968e+01]\n",
      " [  1.09432151e+02]\n",
      " [ -1.21007300e+02]]\n"
     ]
    }
   ],
   "source": [
    "print(\"score of test data is better\")\n",
    "bY_predict=hypothesis(X_test, bfinal_theta)\n",
    "print(bY_predict) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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